The B.Tech in Artificial Intelligence and Machine Learning is specialisation program designed to enable students to build intelligent machines, software, or applications with a cutting-edge combination of machine learning, analytics and visualisation technologies. The course will include study of algorithms, signal processing, robotics and mathematical foundations, AI methods based in different fields, including neural networks, data mining, in order to present an integrated treatment of machine learning problems and solutions. The course also provides abundant opportunities to students to work on self-designed mini-projects, develop communication skills, explore internship opportunities in industry and take part in national and international conferences and circuit/Software design contests. The department is committed to promote research, industrial interaction and multi-dimensional development of the students with theoretical as well as practical exposure.
Graduation in Artificial Intelligence and Machine Learning provides career roles as
| SEMESTER 1 | SEMESTER 2 | ||||
| S. No. | Subject | Credit/ Marks | S. No | Subject | Credit/ Marks |
| 1 | Engineering Physics of Materials | 4/100 | 1 | Engineering Chemistry of Materials | 4/100 |
| 2 | Computer Software Concept & Programming | 4/100 | 2 | Information Communication & Computation Technology | 4/100 |
| 3 | Elements of Electrical & Electronics | 4/100 | 3 | Elements of Mechanical and Civil Engineering | 4/100 |
| 4 | Quantitative Techniques in Engineering –I/II | 4/100 | 4 | Quantitative Techniques in Engineering –I/ II | 4/100 |
| 5 | Lab on Engineering Physics of Materials | 1.5/100 | 5 | Lab on Engineering Chemistry of Materials | 1.5/100 |
| 6 | Electrical & Electronics Lab | 2/100 | 6 | Computer Aided Engineering Drawing Lab | 2/100 |
| 7 | Lab on Computer Programming | 1.5/100 | 7 | Lab on Spreadsheet Programming | 1.5/100 |
| 8 | Technical English (ESEP – Xlanz) | 2/50 M | 8 | Professional English ( ESEP – Xlanz) 2/50 M | |
| 9 | Principles of Environmental Studies | 2/50 M | 9 | Constitution & Professional Ethics | 2/50 M |
| 10 | Kannada/ Co-curricular Activities/Sports (ESEP) | - | 10 | Kannada/ Co-curricular Activities/Sports (ESEP) | - |
| Total Credit | 25/800 | Total Credit | 25/800 | ||
| SEMESTER 3 | SEMESTER 4 | ||||
| Sl. No. | Subject | Credit/ Marks | Sl. No. | Subject | Credit/ Marks |
| 1 | Engineering Statistics | 4/100 | 1 | Discrete Mathematical Structures and Graph Theory | 4/100 |
| 2 | Data Structures and its Applications using C | 3/100 | 2 | Design and Analysis of Algorithms | 3/100 |
| 3 | Computer Architecture and Design | 3/100 | 3 | Database Management System | 3/100 |
| 4 | Object Oriented Programming using JAVA | 3/100 | 4 | Computer Network | 3/100 |
| 5 | Foundations of Artificial Intelligence | 3/100 | 5 | Fundamentals of Machine Learning | 3/100 |
| 6 | Data Structures using C Lab | 2/100 | 6 | Database Management Systems lab | 2/100 |
| 7 | Oops Using Java Lab | 2/100 | 7 | Computer Network Lab | 2/100 |
| 8 | Employability Skills Enhancement Programme 1 (ESEP 1) | 2/50 | 8 | Employability Skills Enhancement Programme – 2 (ESEP – 2) | 2/50 |
| 9 | International Certification Course on Current Trends - 1 | 1/0 | 9 | International Certification Course on Current Trends - II | 1/0 |
| Total Credits/ Total Marks | 23/750 | Total Credits/ Total Marks | 23/750 | ||
| SEMESTER 5 | SEMESTER 6 | ||||
| Sl. No. | Subject | Credit/ Marks | Sl. No. | Subject | Credit/ Marks |
| 1 | Operating Systems | 3/100 | 1 | Web Technology | 3/100 |
| 2 | Microprocessor and Interfacing Techniques | 3/100 | 2 | Foundations of Deep Learning | 3/100 |
| 3 | Introduction to Artificial Neural network | 3/100 | 3 | Core Elective - 1 | 3/100 |
| 4 | Introduction to Natural Language Processing | 3/100 | 4 | Core Elective - 2 | 3/100 |
| 5 | Computer Graphics | 3/100 | 5 | Open Elective -1 | 3/100 |
| 6 | Operating System Lab | 2/100 | 6 | Machine Learning Lab | 2/100 |
| 7 | Artificial Intelligence Lab | 2/100 | 7 | Fundamental of IOT Lab | 2/100 |
| 8 | Employability Skills Enhancement Programme 3 (ESEP 3) | 2/50 | 8 | Employability Skills Enhancement Programme 4 (ESEP – 4) | 2/50 |
| 9 | Internship – I | 2/50 | 9 | MOOC – 2 (Department Specific) | 1/50 |
| 10 | MOOC – 1 (Department Specific) | 1/50 | 10 | International Certification Course on Current Trends – 4 | 1/0 |
| 11 | International Certification Course on Current Trends - 3 | 1/0 | |||
| Total Credits/ Total Marks | 25/850 | Total Credits/ Total Marks | 25/850 | ||
| Sl. No. | Core Elective – 1 | Sl. No. | Core Elective – 2 | Sl. No. | Core Elective – 3 |
| 1 | Fundamental of IoT | 1 | Computer Vision | 1 | Business Intelligence |
| 2 | Computational Intelligence | 2 | Image Processing | 2 | Introduction to Entrepreneurship |
| 3 | Distributed Computing | 3 | Network and System Security | 3 | Introduction to Business Management |
| SEMESTER 7 | ||
| Sl. No. | Subject | Credit/ Marks |
| 1 | Cloud Computing | 3/100 |
| 2 | Data Science | 3/100 |
| 3 | Information Security | 3/100 |
| 4 | Core Elective - 3 | 3/100 |
| 5 | Big Data Analytics | 3/100 |
| 6 | Cloud Computing Lab | 2/100 |
| 7 | Mini Project | 2/100 |
| 8 | Internship – II | 2/50 |
| 9 | ESEP: Patent Filing & IPR | 2/50 |
| 10 | Social Internship | 0/100 |
| Total Credits/ Total Marks | 23/900 | |
| Sl. No. | Core Elective – 3 | |
| 1 | AI in Cyber Security | |
| 2 | High Performance Computing | |
| 3 | Introduction to Soft computing | |
| SEMESTER 8 | ||
| Sl. No. | Subject | Credit/ Marks |
| 1 | Technical Seminar | 2/100 |
| 2 | MOOC – 3 – (Research Methodology) | 1/50 |
| 3 | Project (With Patent Application) | 12/200 |
| Total Credits/ Total Marks | 15/350 | |
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